Core Concepts
The author introduces the concept of uniform classification and proposes a loss function based on binary cross-entropy (BCE) integrated with a unified bias to improve model performance in uniform classification tasks.
Abstract
The paper discusses the concept of uniform classification, introducing a new loss function based on binary cross-entropy with a unified bias. The experiments conducted on various deep learning models show that this approach outperforms traditional SoftMax loss in terms of accuracy. The study highlights the importance of a unified threshold for distinguishing positive and negative metrics across all samples, leading to better performance in open-set tasks like face recognition.
The content delves into the mathematical derivation of a loss function suitable for uniform classification, emphasizing the need for a unified threshold. It compares different loss functions and their impact on model training and performance, showcasing the benefits of using BCE loss with a unified bias. The results from experiments on ImageNet-1K dataset demonstrate the effectiveness of this approach in improving classification accuracy.
Overall, the paper provides valuable insights into enhancing model performance through uniform classification and introduces an innovative approach using BCE loss with a unified bias.
Stats
Compared to SoftMax loss, models trained with BCE loss exhibit higher uniform classification accuracy.
The learned bias from BCE loss is close to the unified threshold used in uniform classification.
Extensive experiments show superior performance of models trained with BCE loss on various datasets and feature extraction models.